Method to Enhance Network Performance Using a Task-Agnostic Graph Abstraction and Afms

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Embodiments disclosed herein generally relate to wireless networks. More particularly, at least some embodiments relate to systems, hardware, software, computer-readable media, and methods, for the use of GNNs (graph neural networks) to enhance network performance using a task-agnostic graph abstraction and AFM (agentic foundation model) reference architecture.

Existing GNN modelling techniques while somewhat successful in some domains, still fall short in wireless applications such as, for example, a heterogenous, and large, wireless network, such as a telco network for example. This is due at least in part to the extensive dependencies among the network components. as well as to the physical environment. In particular, such GNNs require careful model design, feature engineering, and training to capture the complex inter-dependencies among the network components. Moreover, GNNs are mainly designed for a single specific downstream task. Thus, in conventional approaches, a GNN must be re-designed, and re-trained, for each new task to be accomplished in the wireless network.

Embodiments disclosed herein generally relate to wireless networks. More particularly, at least some embodiments relate to systems, hardware, software, computer-readable media, and methods, for the use of GNNs (graph neural networks) to enhance network performance using a task-agnostic graph abstraction and AFM (agentic foundation model) reference architecture.

One or more example embodiments may comprise a method and/or architecture to enhance the performance of a network, such as a wireless communication network in the form of a RAN (radio access network) or O-RAN (open radio access network) for example, performance using a task-agnostic graph abstraction, and one or more AFMs. In an embodiment, a GNN may be employed for handling a variety of different tasks. Because the GNN may be task-agnostic, the GNN may not require reconfiguration, or retraining, to perform a new task not currently performed by the GNN and/or for which the GNN is not configured.

x r d One example embodiment may comprise various operations, including: constructing an enhanced network graph based on attributes of a physical RAN – the network graph, which may take the form of a KG (knowledge graph comprising nodes that each correspond to a respective network entity, and edges that indicate relationships between nodes connected by the edges) may be created using multi-modal information obtained from sources such as RF (radio frequency) signals, camera images, video data, and LiDAR (light detecting and ranging) images; feeding the network graph to a GNN model which uses the network graph to obtain, and output, network state embeddings; feeding the GNN output to a prompt generator which uses the GNN output to create a prompt; providing the prompt to an AFM; passing an output of the AFM, obtained as a result of the prompt, to a downstream task in a network automation platform, such asAPP,APP, and/orAPP; processing the AFM response for the downstream task; and, sending a control command, corresponding to the task, into the RAN.

Embodiments, such as the examples disclosed herein, may be beneficial in a variety of respects. For example, and as will be apparent from the present disclosure, one or more embodiments may provide one or more advantageous and unexpected effects, in any combination, some examples of which are set forth below. It should be noted that such effects are neither intended, nor should be construed, to limit the scope of the claims in any way. It should further be noted that nothing herein should be construed as constituting an essential or indispensable element of any embodiment. Rather, various aspects of the disclosed embodiments may be combined in a variety of ways so as to define yet further embodiments. For example, any element(s) of any embodiment may be combined with any element(s) of any other embodiment, to define still further embodiments. Such further embodiments are considered as being within the scope of this disclosure. As well, none of the embodiments embraced within the scope of this disclosure should be construed as resolving, or being limited to the resolution of, any particular problem(s). Nor should any such embodiments be construed to implement, or be limited to implementation of, any particular technical effect(s) or solution(s). Finally, it is not required that any embodiment implement any of the advantageous and unexpected effects disclosed herein.

In particular, one advantageous aspect of at least some embodiments is that a GNN may be employed for decision making that is agnostic as to the particular task(s) which are to be implemented in a communication network. An embodiment may eliminate any need to separately retrain one or more GNNs for each different downstream task to be performed in a communication network. An embodiment may effectively and efficiently capture node interdependencies of a large scale, and complex, RAN, so as to serve various downstream tasks. Various other advantages of one or more example embodiments will be apparent from this disclosure.

One or more embodiments may apply GenAI (generative artificial intelligence) in the context of communication network environments, such as an O-RAN for example. In this regard, AI/ML (artificial intelligence / machine learning) for communication applications and environments, such as O-RANs for example, faces various challenges, particularly in large scale deployments. Such challenges include generalization limitations, such as for new network topologies and conditions, and obtaining proper coordination of multi multi-vendor multi multi-agent solutions.

However, it may be expected that recent progress on GenAI, and LLMs in particular, will open a new era in wireless network optimization by providing unprecedented orchestration and generalization capabilities. In the longer term, GenAI may also help to shape new 6G, and subsequent, paradigms such as semantic communications.

Moreover, the approach to telecom standardization may change, possibly significantly. For example, instead of specifying granular elements of network protocols, new telecom standardization approaches may move instead towards only defining high level concepts, such as slicing for example, and leaving the lower-level granular implementation to GenAI platforms.

100 2018 2023 1 2018 4 2023 1 FIG. As shown in the example graphdisclosed in, the size and architecture of LLMs has progressed significantly betweenand. This is particularly true in the areas of encoder-decoder developments, and decoder-only developments. For example, GPT-was a key technology in, but has been overtaken by GPT-as of. By way of contrast, the pace of development of encoder-only platforms has been somewhat slower than that of the decoder-only platforms, and slower than that of the encoder-decoder platforms.

One or more embodiments may employ an AFM in the context of O-RAN operations. As used herein, a Telco Agentic Foundation Model (AFM) may comprise a GenAI module fine-tuned on telecom data which may include multiple functions to support its decision-making ability including, but not limited to, specialized AI/ML agents, knowledge base, and digital twins. One or more embodiments may employ such a GenAI module for functions including, but not limited to, prompt generation, and network configuration definition and refinement.

2 FIG. 200 200 With reference now to, an example AFM reference architecture, in which one or more example embodiments may be implemented, is disclosed. This example AFM reference architecturedescribes various processes and interactions between various components and agents leveraging different knowledge bases, and implementing multiple different LLM instances tuned to achieve specific objectives. Such objectives may include, for example, network operations, network DT (digital twin) and data management, GNN (Graph Neural Network) for network optimization, customer support, and performance evaluation and training.

200 202 204 206 202 208 204 210 202 208 208 208 a b In more detail, the example architecturemay comprise various inputssuch as a base-level LLMwhich may take the form of an open source based/private multi-modal LLM, and various informationsuch as a telco corpus for example. The inputsmay be provided to a fine-tuning modulethat may tune the base-level LLMto create a more specific LLM implementation, such as a telco, or O-RAN, multi-modal LLM. In addition to the inputs, the fine-tuning modulemay also comprise a dataset, and various instructions, which may both be used in a fine tuning process.

210 212 214 210 210 210 210 210 210 214 216 210 214 210 218 210 a b c d The multi-modal LLMmay operate to define, and orchestrate, such as in cooperation with one or more AI/ML agents, one or more elements of a network configuration, or elements of an O-RAN. To these, and other, ends, the multi-modal LLMmay comprise various components, such as prompt engineering, design support, RAG, and a network orchestration module. In connection with its operations, the multi-modal LLMmay receive various inputs, such as reinforcement learning human feedback, and reinforcement learning network feedback, both of which may be used by the multi-modal LLMto define, implement, and refine, the network configuration. Further, the reinforcement learning feedback, as well as historical log information concerning usage and configuration of networks, may be used to generate forecasts as to changes to the configuration, and use, of one or more private networks. As well, the multi-modal LLMmay draw from, and make deposits to, a knowledge base, concerning the operations of the multi-modal LLM.

214 214 214 214 214 a b c d Finally, the network configurationmay comprise various elements. Such elements may include, but are not limited to, a digital twin, network dataconcerning network operations, events, and configurations, a physical communication network, and a customer support modulewhich may comprise, for example, a virtual assistant such as a chatbot that comprises an LLM.

3 FIG. 300 302 304 304 With attention now to, a comparative example of a schemais provided which, considered together with the rest of this disclosure, may help to illustrate some useful features and aspects of one or more embodiments. As shown there, a network graphmay be generated based on a physical RAN. As noted earlier herein, a network graph may comprise a KG that represents network entities as nodes, and relationships between the entities are represented as edges connecting the nodes. In an embodiment, each node and edge has a corresponding set of features comprised of RF KPIs (key performance indicators) as well as the multi-modal KPIs collected from the physical RAN.

302 306 304 306 302 308 308 310 304 The network graphmay be provided as an input to a GNN modelconfigured for a particular task that is to be implemented in the physical RAN. The GNN modelmay use the network graphto output parameters of the task to one or more network automation apps. The network automation appsmay translate the task parameters into one or more control commandswhich may then be sent to the physical RANfor implementation of the task.

306 304 306 300 In this comparative example, the GNN modelis only configured to perform the designated task. Any new or modified tasks required to be implemented in the physical RANwould require a re-configuration, and retraining, of the GNN model. Notably, as well, the example schemalacks an AFM and its associated functionality.

4 FIG. 400 With attention next to, an example schemaaccording to one embodiment is disclosed. The following discussion will address operations of an example method according to one embodiment, with reference to various architectural elements.

4 FIG. 401 402 404 402 As shown in, an example method may begin with constructionof an enhanced network graph (ENG)based on attributes of a communication network, such as RF signals and multi-modal data (2D Lidar images and sensor data, from a physical RAN. In an embodiment, the ENGmay take the form of a KG that describes fact and conventions in the network as well as the upstream and down stream relations among the network components. This for example includes nodes that each correspond to a particular network entity, which may comprise hardware and/or software, and also includes edges that describe relationships between the nodes that they connect. It is noted that as used herein, a ‘network entity’ may comprise any system or component, whether comprised of hardware and/or software, that may be included in, or at least play a role in, a wireless communication network. As such, and solely for the purposes of illustration, network entities may comprise, but are not limited to, cameras both still and video, LiDAR systems, transmitters, receivers, routers, servers, computing devices of any kind, repeaters, antennas, cell phone towers, and mobile phones, for example.

402 In an embodiment, the ENGmay be constructed using, and may comprise, multi-modal information from and/or about a variety of sources. Such sources may include, but are not limited to, RF signals, camera images, video data, LiDAR images, and sensor data measurements from IoT devices.

402 403 406 402 406 5 FIG. The ENGmay then be providedas an input to a GNN model. In an embodiment, the combination of the ENGand the GNN modelmay be considered as an ‘enhanced GNN model,’ another example of which is discussed below in connection with.

406 406 402 404 404 402 In an embodiment, the GNN modelmay not be configured to perform any particular task(s), but may instead be task-agnostic. The GNN modelmay use the input ENGto generate, and output, network state embeddings that comprise combinations of both RF KPIs and multi-modal network KPIs of particular features of the physical RAN, such as structural features of the physical RAN, to elements, such as the nodes and edges, of the ENG.

406 405 408 406 408 The output of the GNN model, that is, the network state embeddings, may then be providedto a prompt generator, which may take the form of a prompt generator engine in one embodiment. In addition to the GNN modeloutput, a request, which may identify a particular task, or tasks, to be performed, from xApp/rApp/dApp, or simply ‘request,’ may also be provided to the prompt generator.

Briefly, an xAPP refers to an application that may have to execute in near-real time, while an rAPP refers to an application that executes more slowly, that is, in non-real time. By way of example, an xAPP may be an application that handles handovers in a cell network – since one aim may be to provide uninterrupted service for a caller, the xAPP handling handovers should operate in near-real time. As another example, an rAPP may be an application that controls cell tower transmission power – in many networks, the transmission power may not need to change quickly, and as such, the rAPP may operate more slowly than an xAPP, that is, in non-real time. Finally, a dAPP refers to a decentralized application that uses a group of computing devices to provide one or more services, such as network security for example.

408 406 407 410 410 404 410 409 412 412 410 411 404 411 404 2 FIG. x r d The prompt generatormay then use the GNN modeloutput to create a prompt, which may be providedto an AFM, another example of which is disclosed in. The AFMmay act on the prompt, which may include or reference the network state embeddings described above, and possibly using a self-attention mechanism, capture entity interdependencies of the physical RANsuch as may be needed by one or more downstream tasks. The entity interdependencies obtained by the AFMin response to the prompt may then be transmittedto a downstream task in a network platform, such asAPP,APP, and/orAPP. The network platformmay process the information received from the AFMand generate, and transmit, a control command, corresponding to the task(s) to be performed, to the physical RAN. The control command(s)may then be executed by one or more network elements to perform the task(s) in the physical RAN.

5 FIG. 4 FIG. 500 500 502 504 506 508 With reference next to, an example schemaaccording to one embodiment is disclosed. As shown, the schemamay comprise a physical RAN, enhance GNN model, prompt generator, and AFM. In one embodiment, any one or more of these components may have similar, or identical, features and functionalities to their counterparts in.

5 FIG. 508 506 510 510 1 510 2 510 502 510 512 502 502 n As shown in the example, information generated by the AFMbased on a prompt received from the prompt generatormay be passed to a clusterof downstream tasks-,-…-that are to be performed in the physical RAN. Each of the tasks in the clustermay generate a respective control commandwhich may be transmitted, synchronously or asynchronously, to the physical RAN. The various control commands 502 may perform, and/or cause the performance of, the various tasks in the physical RAN.

5 FIG. 504 510 502 504 504 It is noted with respect to the example ofat least, that a single, task-agnostic enhanced GNN modelmay be used to effect the implementation of multiple different tasks, exemplified by the cluster, in the physical RAN. Advantageously, the same GNN modelmay be used for all of the tasks, without requiring reconfiguration or retraining of the GNN modelto suit the various different tasks.

As disclosed herein, one or more embodiments may comprise various useful features and aspects, although no embodiment is required to possess any of such features or aspects. The following examples are illustrative, but not exhaustive.

In an embodiment, an enhanced GNN model is agnostic to downstream tasks and only embeds the network features in a latent space. As such, an embodiment may eliminate the need to re-train GNNs for each downstream task separately. In an embodiment, using network feature embedding, and leveraging a self-attention mechanism of an AFM, the AFM can efficiently capture the inter-dependencies in a large scale and complex RAN to serve the downstream tasks. An embodiment may provide flexibility and generalizability in serving various downstream tasks. An embodiment may implement customization on prompts based on the parameters and features of the planned downstream tasks. Finally, a single AFM may serve a cluster of downstream tasks.

It is noted that any operation(s) of any of the methods disclosed herein, may be performed in response to, as a result of, and/or, based upon, the performance of any preceding operation(s). Correspondingly, performance of one or more operations, for example, may be a predicate or trigger to subsequent performance of one or more additional operations. Thus, for example, the various operations that may make up a method may be linked together or otherwise associated with each other by way of relations such as the examples just noted. Finally, and while it is not required, the individual operations that make up the various example methods disclosed herein are, in some embodiments, performed in the specific sequence recited in those examples. In other embodiments, the individual operations that make up a disclosed method may be performed in a sequence other than the specific sequence recited.

Following are some further example embodiments. These are presented only by way of example and are not intended to limit the scope of this disclosure or the claims in any way.

Embodiment 1. A method, comprising: generating an enhanced network graph that comprises a representation of a physical RAN (radio access network); providing the enhanced network graph as input to a GNN (graph neural network) model; with the GNN model, using the enhanced network graph, to obtain network state embeddings of the physical RAN; providing the network state embeddings and a cluster of network task requests to a prompt generator; by the prompt generator, creating a prompt based on the network state embeddings and network task requests; transmitting the prompt to an AFM (agentic foundation model); based on the prompt, capturing, by the AFM, entity interdependencies of the physical RAN; transmitting the prompt, by the AFM, to a cluster of downstream tasks that each correspond to a respective one of the network task requests; and by the cluster of downstream tasks, generating respective control commands and transmitting the control commands to the physical RAN.

Embodiment 2. The method as recited in any preceding embodiment, wherein the enhanced network graph comprises nodes that each represent a respective entity of the physical RAN, and further comprises edges that connect the nodes and represent relationships between nodes that are so connected.

Embodiment 3. The method as recited in any preceding embodiment, wherein the GNN model is task-agnostic.

Embodiment 4. The method as recited in any preceding embodiment, wherein the control commands are executable in the physical RAN to implement the network tasks.

x r d Embodiment 5. The method as recited in any preceding embodiment, wherein the downstream tasks in the cluster correspond to one of anAPP, anAPP, or aAPP.

Embodiment 6. The method as recited in any preceding embodiment, wherein the network state embeddings comprise respective correlations of particular features of the physical RAN to elements of the enhanced network graph.

Embodiment 7. The method as recited in embodiment 6, wherein the GNN model embeds the features of the physical RAN in a latent space.

Embodiment 8. The method as recited in any preceding embodiment, wherein the GNN model does not need to be individually trained for each of the downstream tasks.

Embodiment 9. The method as recited in any preceding embodiment, wherein addition of a new downstream task to the cluster of downstream tasks does not require reconfiguration or retraining of the GNN model.

Embodiment 10. The method as recited in any preceding embodiment, wherein the control commands are executed in the physical RAN to implement the network tasks.

Embodiment 11. A system, comprising hardware and/or software, operable to perform any of the operations, methods, or processes, or any portion of any of these, disclosed herein.

Embodiment 12. A non-transitory storage medium having stored therein instructions that are executable by one or more hardware processors to perform operations comprising the operations of any one or more of embodiments 1-10.

The embodiments disclosed herein may include the use of a special purpose or general-purpose computer including various computer hardware or software modules, as discussed in greater detail below. A computer may include a processor and computer storage media carrying instructions that, when executed by the processor and/or caused to be executed by the processor, perform any one or more of the methods disclosed herein, or any part(s) of any method disclosed.

As indicated above, embodiments within the scope of this disclosure also include computer storage media, which are physical media for carrying or having computer-executable instructions or data structures stored thereon. Such computer storage media may be any available physical media that may be accessed by a general purpose or special purpose computer.

By way of example, and not limitation, such computer storage media may comprise hardware storage such as solid state disk/device (SSD), RAM, ROM, EEPROM, CD-ROM, flash memory, phase-change memory (“PCM”), or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other hardware storage devices which may be used to store program code in the form of computer-executable instructions or data structures, which may be accessed and executed by a general-purpose or special-purpose computer system to implement the disclosed functionality. Combinations of the above should also be included within the scope of computer storage media. Such media are also examples of non-transitory storage media, and non-transitory storage media also embraces cloud-based storage systems and structures, although the scope of this disclosure is not limited to these examples of non-transitory storage media.

Computer-executable instructions comprise, for example, instructions and data which, when executed, cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. As such, some embodiments may be downloadable to one or more systems or devices, for example, from a website, mesh topology, or other source. As well, the scope of this disclosure embraces any hardware system or device that comprises an instance of an application that comprises the disclosed executable instructions.

Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts disclosed herein are disclosed as example forms of implementing the claims.

As used herein, the term module, component, client, agent, service, engine, or the like may refer to software objects or routines that execute on the computing system. These may be implemented as objects or processes that execute on the computing system, for example, as separate threads. While the system and methods described herein may be implemented in software, implementations in hardware or a combination of software and hardware are also possible and contemplated. In the present disclosure, a ‘computing entity’ may be any computing system as previously defined herein, or any module or combination of modules running on a computing system.

In at least some instances, a hardware processor is provided that is operable to carry out executable instructions for performing a method or process, such as the methods and processes disclosed herein. The hardware processor may or may not comprise an element of other hardware, such as the computing devices and systems disclosed herein.

In terms of computing environments, embodiments may be performed in client-server environments, whether network or local environments, or in any other suitable environment. Suitable operating environments for at least some embodiments include cloud computing environments where one or more of a client, server, or other machine may reside and operate in a cloud environment.

6 FIG. 1 5 FIGS.- 6 FIG. 600 With reference briefly now to, any one or more of the entities disclosed, or implied, by, and/or elsewhere herein, may take the form of, or include, or be implemented on, or hosted by, a physical computing device, one example of which is denoted at. As well, where any of the aforementioned elements comprise or consist of a virtual machine (VM), that VM may constitute a virtualization of any combination of the physical components disclosed in.

6 FIG. 600 602 604 606 608 610 612 602 600 614 606 In the example of, the physical computing deviceincludes a memorywhich may include one, some, or all, of random access memory (RAM), non-volatile memory (NVM)such as NVRAM for example, read-only memory (ROM), and persistent memory, one or more hardware processors, non-transitory storage media, UI device, and data storage. One or more of the memory componentsof the physical computing devicemay take the form of solid state device (SSD) storage. As well, one or more applicationsmay be provided that comprise instructions executable by one or more hardware processorsto perform any of the operations, or portions thereof, disclosed herein.

Such executable instructions may take various forms including, for example, instructions executable to perform any method or portion thereof disclosed herein, and/or executable by/at any of a storage site, whether on-premises at an enterprise, or a cloud computing site, client, datacenter, data protection site including a cloud storage site, or backup server, to perform any of the functions disclosed herein. As well, such instructions may be executable to perform any of the other operations and methods, and any portions thereof, disclosed herein.

The described embodiments are to be considered in all respects only as illustrative and not restrictive. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope.